Artificial intelligence (AI) looks set to disrupt every facet of working life, and the pharmaceutical sector is widely touted as being one of the industries that is most heavily impacted.
Among the top stocks for DIY investors during August were AI stocks like Nvidia and Palantir, according to data from Interactive Investor, but pharmaceutical stocks have been less in favour of late.
But the two industries look as though they are going to become ever more closely intertwined.
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AstraZeneca (LON:AZN) is the UK stock most-frequently held in AI funds, observes Kenneth Lamont, principal at Morningstar. “AI applicators – businesses using AI to improve their models, such as AstraZeneca in drug discovery [may] have the last laugh” as regards the AI boom, said Lamont. “By harnessing increasingly powerful and low-cost AI tools to boost margins with little risk, they could prove to be the real long-term winners.”
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GSK (LON:GSK) (which dropped off Interactive Investor’s top stocks list for August) was among the companies to roll out the red carpet during Donald Trump’s state visit to the UK earlier in September as the US president was accompanied by a gaggle of big tech’s top executives, all keen to invest in the UK economy.
GSK has announced a $30 billion investment plan for its US R&D manufacturing capacity.
“There is a new $1.2 billion investment in the package and GSK are keen to stress that introducing AI technology into their manufacturing across the States is part of the plan,” said Steve Clayton, head of equity funds at Hargreaves Lansdown.
How are pharmaceutical companies using AI?
The potential of AI to analyse vast quantities of data in a relatively short time frame makes its application to the pharmaceutical industry, particularly the time- and cost-intensive process of drug discovery, potentially transformative.
“Data science and AI are transforming R&D, helping us turn science into medicine more quickly and with a higher probability of success,” said Jim Weatherall, chief data scientist, BioPharmaceuticals R&D at AstraZeneca, adding that the company is “applying AI throughout the discovery and development process, from target identification to clinical trials.”
The FT recently reported that progress on AI-driven drug discovery has been slower than had been hoped over the last ten years or so. That is in large part because there are still crucial areas of biology, such as how cells and drugs interact, that aren’t sufficiently understood by scientists to give AI models the data they need to make accurate predictions.
But developments like AlphaFold2, a system launched by Google DeepMind in 2021 that can predict the shape of proteins from the sequence of their amino acids, could mark a turning point.
Will AI end big pharma?
A new report from Causeway Capital entitled ‘Will AI Make Big Pharma Obsolete?’ argues that AI is likely to have a significant impact on the sector, but that ultimately it will play into the hands of the industry’s largest incumbents.
“Artificial intelligence innovators and investors, like Sam Altman and Vinod Khosla, argue that drugs of the future will be built by small, nimble teams – sometimes fewer than 20 people – wielding AI,” write the report’s authors Alessandro Valentini and Steve Nguyen, both portfolio managers at Causeway Capital.
In their view, “AI will compress discovery timelines and reduce the cost of identifying new molecules, but it will not eliminate the most complex, value-dense stages of the drug business.”
Small teams, they argue, cannot easily replicate the laborious process of turning a promising molecule into a globally approvable medicine. In fact, the scale advantages that big pharma companies enjoy could even be amplified by the effect of AI reducing the time and cost of the drug discovery phase.
“If AI truly delivers higher-quality drug candidates, the probability of success for early-stage assets rises,” said Valentini and Khosla. “This means large companies could see greater returns from the same level of investment, particularly when bringing in programs prior to large-scale proof-of-concept trials.”
This could lead to a decline in the cost of early-stage R&D even as its value increases, which would reinforce big pharma’s role as a consolidator and scale operator.
“AI is not a death sentence for big pharma,” Valentini and Khosla conclude. “Instead, it should be a catalyst for reshaping the economics of the industry, likely to the benefit of shareholders. Small biotech teams may generate more discoveries, but they should continue to rely on large incumbents to manufacture at scale, run global trials, navigate complex regulatory environments, and bring medicines to patients worldwide.”
